library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
library(destiny)

coi <- params$cell_type_super
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster

1 Cluster markers

1.1 Major Myeloid.super markers for cell assign

### load all data ---------------------------------
source("_src/global_vars.R")

# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/", coi, "_seurat_", louvain_resolution, ".rds"))
# seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_highqc.rds"))

myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")

plot_data_wrapper <- function(cluster_res) {
  cluster_res <- enquo(cluster_res)
  as_tibble(FetchData(seu_obj, myfeatures)) %>% 
    left_join(meta_tbl, by = "sample") %>% 
    rename(cluster = !!cluster_res) %>% 
    mutate(cluster = as.character(cluster),
           tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}

plot_data <- plot_data_wrapper(louvain_cluster)

1.2 Subtype currated markers

helper_f <- function(x) ifelse(is.na(x), "", x)

markers_v7_super[[coi]] %>% 
  group_by(subtype) %>% 
  mutate(rank = row_number(gene)) %>% 
  spread(subtype, gene) %>% 
  mutate_all(.funs = helper_f) %>% 
  formattable::formattable()
rank cDC1 cDC2 Cycling.M dissociated M1.CDKN1C M1.CXCL10 M1.S100A8 M1.S100A9 M2.MARCO M2.SELENOP Mast.cell mDC Neutrophil pDC
1 BATF3 CD1C CDC20 BTG1 CD52 CCL8 APOBEC3A AREG CCL20 A2M CPA3 BIRC3 CSF3R BCL11A
2 BTLA CLEC10A CDK1 DNAJB1 CDKN1C CXCL10 FCN1 EREG CXCL3 CCL3L1 CTSG CCL19 CXCR2 CLIC3
3 CLEC9A FCER1A MKI67 DUSP1 LST1 CXCL11 S100A8 S100A8 FN1 CCL4L2 GATA2 CCR7 FCGR3B GZMB
4 IRF8 HLA-DQA1 PTTG1 EGR1 SMIM25 IFIT1 S100A9 S100A9 HAMP FCGBP HPGD IDO1 IL1R2 JCHAIN
5 XCR1 S100B TOP2A FOS TCF7L2 TNFSF10 VCAN THBS1 LYVE1 SELENOP HPGDS MARCKSL1 NAMPT PTGDS
6 FOSB MARCO KIT TXNIP
7 HSPA1A PLIN2 MS4A2
8 HSPA1B TGM2 TPSAB1
9 HSPA6 TPSB2
10 JUN
11 JUNB
12 KLF2
13 MT1E
14 MT1G
15 MT1H
16 MT1X

1.3 Subtype cluster markers

# marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>% 
#   filter(resolution == louvain_resolution)
marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/", coi, "_markers_", louvain_resolution, ".tsv"))
# marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_highqc_markers_02.tsv"))

## Hypergeometric test --------------------------------------

test_set <- marker_tbl %>% 
  group_by(cluster) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(k = length(cluster)) %>% 
  ungroup %>%
  select(cluster, gene, k) %>% 
  mutate(join_helper = 1) %>% 
  group_by(cluster, join_helper, k) %>%
  nest(test_set = gene)

markers_doub_tbl <- markers_v7 %>% 
  enframe("subtype", "gene") %>% 
  filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>% 
  unnest(gene) %>% 
  group_by(gene) %>% 
  filter(length(gene) == 1) %>% 
  mutate(subtype = paste0("doublet.", subtype)) %>% 
  bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))

ref_set <- markers_v7_super[[coi]] %>% 
  bind_rows(markers_doub_tbl) %>% 
  group_by(subtype) %>% 
  mutate(m = length(gene),
         n = length(rownames(seu_obj))-m,
         join_helper = 1) %>% 
  group_by(subtype, m, n, join_helper) %>%
  nest(ref_set = gene)

hyper_tbl <- test_set %>% 
  left_join(ref_set, by = "join_helper") %>% 
  group_by(cluster, subtype, m, n, k) %>%
  do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
  mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
  ungroup %>%
  mutate(qval = p.adjust(pval, "BH"),
         sig = qval < 0.01)

# hyper_tbl %>% 
#   group_by(subtype) %>% 
#   filter(any(qval < 0.01)) %>%
#   ggplot(aes(subtype, -log10(qval), fill = sig)) +
#   geom_bar(stat = "identity") +
#   facet_wrap(~cluster) +
#   coord_flip()
  
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
  
cluster_label_tbl <- hyper_tbl %>% 
  mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>% 
  arrange(qval, subtype) %>%
  group_by(cluster) %>% 
  slice(1) %>% 
  mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>% 
  select(cluster, cluster_label = subtype) %>% 
  ungroup %>% 
  mutate(cluster_label = make.unique(cluster_label, sep = "_"))

seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label

cluster_n_tbl <- seu_obj$cluster_label %>% 
  table() %>% 
  enframe("cluster_label", "cluster_n") %>% 
  mutate(cluster_nrel = cluster_n/sum(cluster_n))

marker_sheet <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  group_by(cluster_label) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(rank = row_number(-avg_logFC)) %>% 
  select(cluster_label, gene, rank) %>% 
  ungroup %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  spread(cluster_label, gene) %>% 
  mutate_all(.funs = helper_f)

marker_tbl_annotated <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  left_join(cluster_n_tbl, by = "cluster_label") %>% 
  select(-cluster, -resolution) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  arrange(cluster_label, -avg_logFC, p_val_adj)

write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_marker_sheet.tsv"))

write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_marker_table_annotated.tsv"))

formattable::formattable(marker_sheet)
rank M1.S100A8 M1.CXCL10 M2.SELENOP M2.MARCO cDC1 cDC2 mDC pDC Mast.cell Neutrophil Cycling.M dissociated doublet.Fibroblast doublet.Fibroblast_1 doublet.Ovarian.cancer.cell doublet.T.cell
1 S100A9 CXCL10 SELENOP MT1G CPVL FCER1A BIRC3 GZMB TPSAB1 PLCG2 STMN1 HSPA6 COL1A1 MMP9 SLPI IL32
2 S100A8 CXCL11 SLC40A1 MARCO CLEC9A S100B CCL19 PTGDS TPSB2 C3 H2AFZ HSPA1A COL1A2 CTSK WFDC2 CCL5
3 VCAN CCL8 CCL3L1 MT1X LGALS2 CLEC10A CCR7 JCHAIN CPA3 MTRNR2L12 MKI67 DNAJB1 COL3A1 ACP5 KRT19 CD2
4 FCN1 IFIT2 CCL4L2 MT1H CPNE3 CD1C MARCKSL1 CLIC3 HPGD MTRNR2L8 CENPF HSPH1 CALD1 SLC9B2 LCN2 GZMA
5 S100A12 IFIT1 CCL3 PLIN2 IDO1 AREG TXN BCL11A MS4A2 IGKC HIST1H4C HSPA1B SPARC ANPEP KRT18 CD3D
6 THBS1 TNFSF10 DAB2 CXCL3 CST3 HLA-DPB1 IDO1 ITM2C CTSG CSF3R TOP2A HSPB1 SELENOP SPARC CLU NKG7
7 LYZ ISG15 RNASE1 CCL20 C1orf54 HLA-DQA1 LAMP3 LILRA4 CD69 TUBB HSP90AA1 TAGLN ANKH KRT8 TRAC
8 CD55 RSAD2 A2M FN1 SNX3 PPA1 IL7R IRF7 CLU TUBA1B HSPD1 C1QB LMNA KRT7 GNLY
9 RETN IFIT3 SLCO2B1 TIMP1 WDFY4 HLA-DQB1 PPA1 PLAC8 HPGDS TYMS BAG3 C1QA CD109 CD24 LTB
10 CD52 GBP1 F13A1 MT2A DNASE1L3 HLA-DPA1 CCL22 TCF4 RHEX HMGN2 ZFAND2A C1QC SIGLEC15 S100A1 TRBC2
11 EREG CXCL9 FCGBP CXCL8 IRF8 JAML CCL17 TCL1A KIT HMGB2 G0S2 FOLR2 TNFRSF11A S100A13 IL7R
12 AC020656.1 MX1 CCL4 MT1E S100B CD1E EBI3 IGKC SLC18A2 HMGB1 PLIN2 NNMT ANXA2 IGFBP2 TRBC1
13 CD300E APOBEC3A LGMN CXCL2 RGCC HLA-DRA FSCN1 IRF8 VWA5A PCLAF IER5 DCN VIM C19orf33 CD3G
14 APOBEC3A CCL2 APOE NUPR1 CLNK HLA-DRB1 NUB1 IL3RA GATA2 NUSAP1 SERPINH1 F13A1 ATP6V0D2 MDK CD3E
15 AREG ISG20 HTRA1 HAMP XCR1 HLA-DQA2 DUSP5 CCDC50 AREG PTTG1 ADM SLC40A1 CD99 CLDN3 GZMK
16 CFP GBP4 C1QB EREG HLA-DPB1 PKIB CSF2RA TSPAN13 IL1RL1 CKS1B HSPE1 ACTA2 SUCNR1 EPCAM SYNE2
17 SMIM25 HERC5 C1QC CTSL CCND1 LGALS2 CST7 UGCG LTC4S ASPM DNAJA4 GPR34 MATK MMP7 ETS1
18 MCEMP1 GBP5 GPR34 LYVE1 RAB7B HLA-DRB5 BASP1 PPP1R14B HDC PCNA SLC2A3 SLCO2B1 MMP14 MAL2 CXCL13
19 S100A4 IFIH1 FOLR2 CFD NAAA CSF2RA DAPP1 PLD4 LMO4 TK1 CRYAB TMEM176B HCFC1R1 MUC1 CD7
20 RIPOR2 NT5C3A C1QA CYP27A1 HLA-DPA1 HLA-DQB2 CD83 C12orf75 RGS13 SMC4 HILPDA APOE MYO1B ELF3 KLRB1
21 CD36 IL4I1 STAB1 ERO1A BATF3 HERPUD1 GPR157 MZB1 ADCYAP1 TPX2 CACYBP A2M DPP4 LAPTM4B SPOCK2
22 S100A6 MX2 MRC1 ADM HLA-DQB1 CST3 HLA-DQA1 TPM2 AL157895.1 CDK1 HK2 IGFBP2 COL6A1 S100A16 CLEC2D
23 CYP1B1 LY6E MAF G0S2 HLA-DQA1 PAK1 PTPN1 DERL3 IER2 DUT CSTB COL6A2 ITGA2 CRABP2 CST7
24 SERPINA1 IFI6 MS4A7 HK2 TXN CIITA CFLAR LTB SOCS1 TUBA1C UBB MRC1 COL6A2 BCAM TNFRSF18
25 STXBP2 SAMD9L C3 SLC2A5 SLAMF7 LTB LGALS2 SPIB ANXA1 BIRC5 HSP90AB1 DAB2 NFATC1 TM4SF1 IKZF3
26 LST1 IL1RN APOC1 CSTB PPA1 PLAC8 DUSP4 CLEC4C PPP1R15A KPNA2 DDIT4 MS4A4A AK5 S100A14 CD69
27 LILRA5 OAS1 TREM2 PLTP TACSTD2 CST7 HLA-DPB1 SMPD3 BACE2 NUCKS1 TENT5A TPM2 FNBP1L SNCG ITM2A
28 SLC25A37 PARP14 EGR1 ANGPTL4 SHTN1 CD1A RFTN1 SERPINF1 RAC2 RAN FKBP4 LUM PPIC CP LCK
29 FPR1 EPSTI1 PDK4 P4HA1 ASB2 NDRG2 SLCO5A1 MAP1A LIF UBE2C UBC CTSC MYO1D RBP1 DUSP4
30 MNDA STAT1 NRP1 GPNMB RAB32 GRASP EEF1A1 IRF4 GRAP2 SMC2 DNAJB4 MS4A7 EMP1 DSP TNFRSF4
31 LILRB2 DDX58 PLD3 FTH1 KCNK6 CD74 POGLUT1 SCT MAOB CDKN3 BNIP3 COL6A1 CAVIN3 S100A2 ACAP1
32 TIMP1 IFITM3 MSR1 NDRG1 CST7 FILIP1L LAD1 CYB561A3 CSF1 RANBP1 LDHA HLA-DRB1 AC092484.1 PERP GZMH
33 FGR OASL EGR2 GAPDH CADM1 MS4A6A TRAF1 SOX4 RHOH RRM2 SLC2A1 CD74 COL27A1 MAL CD8A
34 NAMPT USP18 GYPC TNFRSF1B BASP1 LYZ BCL2L14 SELL KRT1 PARP1 NDRG1 IGF1 RAI14 SMIM22 CD27
35 COTL1 IFI44L IFI27 GLUL LSP1 IFITM2 AOC1 APP LAPTM4A TUBB4B UPP1 RARRES2 PPFIBP1 MUC16 TIGIT
36 IRAK3 WARS AKR1B1 SLC2A3 PTPRE EEF1A1 CLIC2 SPCS1 SLC45A3 DEK SLC5A3 MS4A6A PHETA1 CLDN4 CTSW
37 LYST IFI35 IGF1 MT1M GSTP1 HLA-DMA SPIB CCDC186 LAT NASP C15orf48 ITM2B DNM1 MSLN CD52
38 SH3BGRL3 RNF213 CPM BNIP3 CD74 CKLF IL2RG LAMP5 STX3 CLSPN ERO1A HLA-DRA F2R ASS1 KLRD1
39 NCF2 LAP3 CTSD EMP3 FGL2 ALDH2 AL031316.1 IGHM UBB HMMR DNAJA1 HLA-DPB1 DIXDC1 CDKN2A CTLA4
40 CCR2 SAMD9 FCGR2A VCAN FNBP1 EEF1B2 CD1E LINC00996 LAX1 H2AFV RGCC HLA-DPA1 AKAP6 FOLR1 EVL
41 FLNA VAMP5 SLC1A3 LDHA HLA-DRB1 GAPT CD274 SEL1L3 SMYD3 CCNB1 BHLHE40 MYL9 NRIP3 PDZK1IP1 CD247
42 MXD1 XAF1 PLAU BNIP3L TAP1 CD1D PTGIR MAPKAPK2 CNRIP1 CEP55 SERPINE1 CCDC80 RUFY4 SCGB2A1 CCND2
43 LTA4H TNFSF13B PLTP SOD2 ACTG1 STK17B MREG SEC61B MLPH ANP32B PPP1R15A POSTN C1orf21 MYL9 RORA
44 RGS2 TAP1 MS4A4A CTSB EEF1B2 PLD4 SLC7A11 CXCR3 ITM2A SPP1 CHORDC1 COL6A3 LTBP2 KRT17 IL2RB
45 TUBA1A C15orf48 AP1B1 FAM162A HLA-DRA IGSF6 TVP23A GPR183 RAB27B TMPO IL1RN CTHRC1 ZNF462 CRYAB GZMM
46 H3F3A OAS3 GAL3ST4 FTL NAP1L1 IFI30 RHOF OFD1 AL662860.1 LDHB VEGFA CD81 HTRA3 CRIP2 APOBEC3G
47 CEBPB DYNLT1 JUN VIM SUB1 SPINT2 LIMCH1 SLC15A4 CAVIN2 DTYMK TCEAL9 BGN STEAP1 SLC34A2 CD96
48 CSF3R OAS2 SGK1 SLC2A1 DUSP4 BASP1 TIFAB PLP2 ITM2C HTRA1 TNFSF14 CXCL12 F5 CTTN RASGRP1
49 CSTA CCL7 ITM2B APLP2 ICAM3 GPAT3 IRF4 TXN CALB2 ARL6IP1 HIF1A-AS2 THY1 LAT ARHGAP29 CCL4
50 LCP1 CMPK2 CD81 FBP1 TMSB4X GPR183 SLC41A2 SELENOS TIMP3 ATAD2 BCL2A1 NBL1 CTTN SOX17 STK17A

2 Clusters

2.1 sizes

enframe(sort(table(seu_obj$cluster_label))) %>% 
  mutate(name = ordered(name, levels = rev(name))) %>% 
  ggplot() +
  geom_bar(aes(name, value), stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(y = c("#cells"), x = "cluster")

2.2 UMAP

alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)

common_layers_disc <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
  labs(color = "")
)

common_layers_cont <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  scale_color_gradientn(colors = viridis(9)),
  guides(color = guide_colorbar())
)

ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  #facet_wrap(~therapy) +
  ggtitle("Sub cluster")

3 Filtering out doublet and dissociated clusters

3.1 UMAP

my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet|dissociated")]
my_subtypes <- my_subtypes[my_subtypes %in% unique(seu_obj$cluster_label)]
my_subtypes <- my_subtypes[my_subtypes %in% names(clrs$cluster_label[[coi]])]

cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered.rds"))

plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cell_id = colnames(seu_obj_sub),
         cluster_label = ordered(cluster_label, levels = my_subtypes),
         )
  
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label) +
  scale_color_manual(values = clrs$cluster_label[[coi]])

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  # geom_point(aes(umapharmony_1, umapharmony_2), 
  #            color = "grey90", size = 0.01, 
  #            data = select(plot_data_sub, -tumor_supersite)) +
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

write_tsv(select(plot_data_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_embedding.tsv"))

3.2 add module scores and pathway scores

signature_modules_cd8 <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>% 
  gather(module, gene) %>% 
  na.omit() %>% 
  group_by(module) %>% 
  do(gene = c(.$gene)) %>% 
  {setNames(.$gene, .$module)} %>% 
  setNames(paste0(names(.), ".module"))

signature_modules_cin <- yaml::read_yaml("_data/small/signatures/cgas_sting.yaml") %>% 
  .[lapply(., length) > 1] %>% 
  setNames(paste0(names(.), ".module"))

## compute expression module scores
for (i in 1:length(signature_modules_cd8)) {
  seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cd8[i], name = names(signature_modules_cd8)[i])
  seu_obj_sub[[names(signature_modules_cd8)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]]
  seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]] <- NULL
  print(paste(names(signature_modules_cd8)[i], "DONE"))
}
## [1] "CD8.Cytotoxic.module DONE"
## [1] "CD8.Dysfunctional.module DONE"
## [1] "CD8.Naive.module DONE"
## [1] "CD8.Predysfunctional.module DONE"
for (i in 1:length(signature_modules_cin)) {
  seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cin[i], name = names(signature_modules_cin)[i])
  seu_obj_sub[[names(signature_modules_cin)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]]
  seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]] <- NULL
  print(paste(names(signature_modules_cin)[i], "DONE"))
}
## [1] "CIN.module DONE"
## [1] "CIN.responsive.Noncanonical.NFKB.targets.up.module DONE"
## [1] "CIN.responsive.Noncanonical.NFKB.targets.down.module DONE"
## [1] "Noncanonical.NFKB.regulators.up.module DONE"
## [1] "Noncanonical.NFKB.regulators.down.module DONE"
## [1] "Canonical.NFKB.regulators.module DONE"
## [1] "Interferon.regulators.module DONE"
## [1] "EMT.module DONE"
## [1] "Inflammation.module DONE"
## [1] "Motility.and.migration.module DONE"
## [1] "ISG.module DONE"
## [1] "SASP.module DONE"
## [1] "Myofibroblast.module DONE"
## [1] "Fibroblast.module DONE"
## [1] "Mesenchymal.stem.cell.module DONE"
## [1] "Pericytes.module DONE"
## [1] "Smooth.muscle.cell.module DONE"
## [1] "ER.stress.module DONE"
## [1] "IFNg.signaling.module DONE"
## compute progeny scores
progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
  as.matrix %>%
  progeny %>%
  as.data.frame %>%
  as.list

names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))

for (i in 1:length(progeny_list)) {
  seu_obj_sub <- AddMetaData(seu_obj_sub,
                                 metadata = progeny_list[[i]],
                                 col.name = names(progeny_list)[i])
}

write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered_annotated.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_processed_filtered_annotated.rds"))

3.3 marker heatmap

marker_top_tbl <- marker_sheet[,-1] %>% 
  slice(1:10) %>% 
  as.list %>% 
  .[!str_detect(names(.), "doublet")] %>% 
  enframe("cluster_label_x", "gene") %>% 
  unnest(gene)

plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>% 
  gather(gene, value, -c(1:(length(myfeatures)+1))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>% 
  group_by(cluster_label, gene) %>% 
  summarise(value = mean(value, na.rm = T)) %>% 
  group_by(gene) %>% 
  mutate(value = scales::rescale(value)) %>% 
  left_join(marker_top_tbl, by = "gene") %>% 
  mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))

ggplot(plot_data_markers) +
  geom_tile(aes(gene, cluster_label, fill = value)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  facet_grid(~cluster_label_x, scales = "free", space = "free") +
  scale_fill_gradientn(colors = viridis(9)) +
  labs(fill = "Scaled\nexpression") +
  theme(aspect.ratio = 1,
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank())

# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)

3.4 composition

3.4.1 per site

comp_site_tbl <- plot_data_sub %>%
  filter(!is.na(tumor_supersite)) %>% 
  group_by(cluster_label, tumor_supersite) %>%
  tally %>%
  group_by(tumor_supersite) %>%
  mutate(nrel = n/sum(n)*100) %>%
  ungroup

pnrel_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(fill = "Cluster", y = "Fraction [%]", x = "") +
  scale_fill_manual(values = clrs$cluster_label[[coi]])

pnabs_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, n, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(fill = "Cluster", y = "# cells", x = "") +
  scale_fill_manual(values = clrs$cluster_label[[coi]])

plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")

# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)

3.4.2 per sample

comp_tbl_sample_sort <- plot_data_sub %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>% 
  tally %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>% 
  mutate(nrel = n/sum(n)*100,
         nsum = sum(n),
         log10n = log10(n),
         label_supersite = "Site",
         label_mutsig = "Signature",
         label_therapy = "Rx") %>% 
  ungroup %>% 
  arrange(desc(therapy), tumor_supersite) %>% 
  mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>% 
  mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
         tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>% 
  arrange(patient_id) %>% 
  mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))

sample_id_x_tbl <- plot_data_sub %>% 
  mutate(sort_short_x = cell_sort) %>% 
  distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>% 
  arrange(sample_id_x)

comp_tbl_sample_sort %>% 
  mutate(sort_short_x = cell_sort) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>% 
  select(sample_id_x, cluster_label, n, nrel, nsum) %>% 
  left_join(sample_id_x_tbl, by = "sample_id_x") %>% 
  write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_subtype_compositions.tsv"))
ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)

max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]

comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
  if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
  if (y == "n") ylab <- paste0("Number\nof cells")
  p <- ggplot(comp_tbl_sample_sort, 
              aes_string("tumor_subsite_rx", y, fill = "cluster_label")) + 
    facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
    coord_cartesian(clip = "off") + 
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.x = element_blank(),
          axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5, 
                                      margin = margin(0, -0.4, 0, 0, unit = "npc")),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.line.x = element_blank(),
          strip.text.y = element_blank(),
          strip.text.x = element_blank(),
          strip.background.y = element_blank(),
          strip.background.x = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) + 
    labs(x = "",
         y = ylab) +
    guides(fill = FALSE)
  if (y == "nrel") p <- p + 
    geom_bar(stat = "identity") +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, 50, 100), 
                       labels = c("0", "50", "100"))
  if (y == "n") p <- p + 
    geom_bar(stat = "identity", position = position_stack()) +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, ymaxn/2, ymaxn),
                       limits = c(0, ymaxn),
                       labels = c("", ymaxn/2, ymaxn)) +
    expand_limits(y = c(0, ymaxn)) +
    theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
  return(p)
} 

common_label_layers <- list(
  geom_tile(color = "white", size = 0.15),
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.line.x = element_blank(),
        strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc")),
  scale_y_discrete(expand = c(0, 0)),
  labs(y = ""),
  guides(fill = FALSE),
  facet_grid(~patient_id, 
             space = "free", scales = "free")
)

comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id), 
       aes(tumor_subsite_rx, label_supersite, 
           fill = tumor_supersite)) + 
  scale_fill_manual(values = clrs$tumor_supersite) +
  common_label_layers

comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_therapy, 
           fill = therapy)) + 
  scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
  common_label_layers

comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_mutsig, 
           fill = consensus_signature)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  common_label_layers

patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)

comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
  facet_grid(~patient_id, 
             space = "free", scales = "free") +
  coord_cartesian(clip = "off") + 
  theme_void() +
  theme(strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc"))

hist_plot_wrapper <- function(x = "nrel") {
  if (x == "nrel") {
    xlab <- paste0("Fraction of cells [%]")
    bw <- 5
  }
  if (x == "log10n") {
    xlab <- paste0("Number of cells")
    bw <- 0.2
  }
  p <- ggplot(comp_tbl_sample_sort) +
    ggridges::geom_density_ridges(
      aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
      stat = "binline", binwidth = bw, scale = 3) +
    facet_grid(label_supersite~., 
               space = "free", scales = "free") +
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.y = element_blank(),
          axis.line.y = element_blank(),
          strip.text = element_blank(),
          strip.background = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_discrete(expand = c(0, 0)) +
    guides(fill = FALSE) +
    labs(x = xlab)
  if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) + 
    scale_x_continuous(expand = c(0, 0), 
                       labels = function(x) parse(text = paste("10^", x)))
  return(p)
}

pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")

pcomp_grid <- plot_grid(comp_label_patient_id, 
                        pcomp1, pcomp2, 
                        comp_label_site, comp_label_rx, comp_label_mutsig,
                        ncol = 1, align = "v", 
                        rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))

phist1 <- hist_plot_wrapper("log10n")

pcomp_hist_grid <- ggdraw() +
  draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
  draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)

pcomp_hist_grid

# ggsave(paste0("_fig/02_composition_v7_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)

3.4.3 site specific cluster enrichment

comp_tbl_z <- comp_tbl_sample_sort %>% 
  filter(therapy == "pre-Rx",
         !(tumor_supersite %in% c("Ascites", "Other"))) %>% 
  group_by(patient_id, cluster_label) %>% 
  arrange(patient_id, cluster_label, nrel) %>% 
  mutate(rank = row_number(nrel),
         z_rank = scales::rescale(rank)) %>% 
  mutate(mean_nrel = mean(nrel, na.rm = T),
         sd_nrel = sd(nrel, na.rm = T),
         z_nrel = (nrel - mean_nrel) / sd_nrel) %>% 
  ungroup()

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

3.4.4 macrophage diffusion maps

## subset macrophages
seu_obj_mp <- seu_obj_sub %>%
  subset(subset = cluster_label %in% grep("^M1|^M2", unique(seu_obj_sub$cluster_label), value = T)) %>%
  RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")

write_rds(seu_obj_mp, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed.rds")

## subset DCs
seu_obj_dc <- seu_obj_sub %>%
  subset(subset = cluster_label %in% grep("DC|Mast", unique(seu_obj_sub$cluster_label), value = T)) %>%
  RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")

write_rds(seu_obj_dc, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/DCs_processed.rds")

seu_obj_dc <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/DCs_processed.rds")

seu_obj_mp <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed.rds")
root_cell <- "SPECTRUM-OV-031_S1_CD45P_LEFT_FALLOPIAN_TUBE_AACAAGATCATTGCTT"
seu_obj_mp_sub <- subset(seu_obj_mp, cells = c(root_cell, colnames(seu_obj_mp)[colnames(seu_obj_mp)!=root_cell][-1]))

dc_obj <- DiffusionMap(seu_obj_mp_sub@reductions$harmony@cell.embeddings, k = 100)
dc_mat <- dc_obj@eigenvectors
colnames(dc_mat) <- paste0("DC_", 1:ncol(dc_mat))
seu_obj_mp_sub[["DC"]] <- CreateDimReducObject(embeddings = dc_mat, key = "DC_", assay = DefaultAssay(seu_obj_mp_sub))

write_rds(seu_obj_mp_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed_filtered.rds")

dpt_obj <- DPT(dc_obj)
write_rds(dpt_obj, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_sub_dpt.rds")
seu_obj_mp_sub$DPT1 <- dpt_obj$DPT1

write_rds(seu_obj_mp_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed_filtered.rds")
seu_obj_mp_sub <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/Macrophages_processed_filtered.rds")
# plot_data_sub_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
#   left_join(meta_tbl, by = "sample") %>% 
#   mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
#          tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
#   mutate(cell_id = colnames(seu_obj_sub),
#          cluster_label = ordered(cluster_label, levels = my_subtypes),
#          )
#   
# if (cell_sort == "CD45+") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
# }
# 
# if (cell_sort == "CD45-") {
# plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
# }
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
#   common_layers_disc +
#   ggtitle("Sub cluster") +
#   #facet_wrap(~cluster_label) +
#   scale_color_manual(values = clrs$cluster_label[[coi]])
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
#   common_layers_disc +
#   ggtitle("Patient") +
#   # facet_wrap(~therapy) +
#   scale_color_manual(values = clrs$patient_id_short)
# 
# ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
#   # geom_point(aes(umapharmony_1, umapharmony_2), 
#   #            color = "grey90", size = 0.01, 
#   #            data = select(plot_data_sub_sub, -tumor_supersite)) +
#   common_layers_disc +
#   ggtitle("Site") +
#   # facet_wrap(~therapy) +
#   scale_color_manual(values = clrs$tumor_supersite)
# 
# write_tsv(select(plot_data_sub_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/", coi, "_embedding_sub.tsv"))

3.4.5 UMAP & DC

plot_data_mp_sub <- as_tibble(FetchData(seu_obj_mp_sub, c(myfeatures, "cluster_label", "DC_1", "DC_2"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
         tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>% 
  mutate(cell_id = colnames(seu_obj_mp_sub),
         cluster_label = ordered(cluster_label, levels = names(clrs$cluster_label[[coi]])))
  
if (cell_sort == "CD45+") {
plot_data_mp_sub <- filter(plot_data_mp_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_mp_sub <- filter(plot_data_mp_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label) +
  scale_color_manual(values = clrs$cluster_label[[coi]])

ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_mp_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = cluster_label)) + 
  common_layers_disc +
  ggtitle("Sub cluster") +
  #facet_wrap(~cluster_label) +
  scale_color_manual(values = clrs$cluster_label[[coi]])

ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_mp_sub, aes(DC_1, DC_2, color = tumor_supersite)) + 
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

4 session info

devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.2 (2019-12-12)
##  os       Debian GNU/Linux 10 (buster)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Etc/UTC                     
##  date     2021-03-06                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package              * version    date       lib
##  abind                  1.4-5      2016-07-21 [2]
##  ape                    5.3        2019-03-17 [2]
##  assertthat             0.2.1      2019-03-21 [2]
##  backports              1.1.10     2020-09-15 [1]
##  bibtex                 0.4.2.2    2020-01-02 [2]
##  Biobase                2.46.0     2019-10-29 [2]
##  BiocGenerics           0.32.0     2019-10-29 [2]
##  BiocParallel           1.20.1     2019-12-21 [2]
##  bitops                 1.0-6      2013-08-17 [2]
##  boot                   1.3-24     2019-12-20 [3]
##  broom                  0.7.2      2020-10-20 [1]
##  callr                  3.4.2      2020-02-12 [1]
##  car                    3.0-8      2020-05-21 [1]
##  carData                3.0-4      2020-05-22 [1]
##  caTools                1.17.1.4   2020-01-13 [2]
##  cellranger             1.1.0      2016-07-27 [2]
##  class                  7.3-15     2019-01-01 [3]
##  cli                    2.0.2      2020-02-28 [1]
##  cluster                2.1.0      2019-06-19 [3]
##  codetools              0.2-16     2018-12-24 [3]
##  colorblindr          * 0.1.0      2020-01-13 [2]
##  colorspace           * 1.4-2      2019-12-29 [2]
##  cowplot              * 1.0.0      2019-07-11 [2]
##  crayon                 1.3.4      2017-09-16 [1]
##  curl                   4.3        2019-12-02 [2]
##  data.table             1.12.8     2019-12-09 [2]
##  DBI                    1.1.0      2019-12-15 [2]
##  dbplyr                 2.0.0      2020-11-03 [1]
##  DelayedArray           0.12.2     2020-01-06 [2]
##  DEoptimR               1.0-8      2016-11-19 [1]
##  desc                   1.2.0      2018-05-01 [2]
##  destiny              * 3.0.1      2020-01-16 [1]
##  devtools               2.2.1      2019-09-24 [2]
##  digest                 0.6.25     2020-02-23 [1]
##  dplyr                * 1.0.2      2020-08-18 [1]
##  e1071                  1.7-3      2019-11-26 [1]
##  ellipsis               0.3.1      2020-05-15 [1]
##  evaluate               0.14       2019-05-28 [2]
##  fansi                  0.4.1      2020-01-08 [2]
##  farver                 2.0.3      2020-01-16 [1]
##  fitdistrplus           1.0-14     2019-01-23 [2]
##  forcats              * 0.5.0      2020-03-01 [1]
##  foreign                0.8-74     2019-12-26 [3]
##  formattable            0.2.0.1    2016-08-05 [1]
##  fs                     1.5.0      2020-07-31 [1]
##  future                 1.15.1     2019-11-25 [2]
##  future.apply           1.4.0      2020-01-07 [2]
##  gbRd                   0.4-11     2012-10-01 [2]
##  gdata                  2.18.0     2017-06-06 [2]
##  generics               0.0.2      2018-11-29 [2]
##  GenomeInfoDb           1.22.0     2019-10-29 [2]
##  GenomeInfoDbData       1.2.2      2020-01-14 [2]
##  GenomicRanges          1.38.0     2019-10-29 [2]
##  ggplot.multistats      1.0.0      2019-10-28 [1]
##  ggplot2              * 3.3.2      2020-06-19 [1]
##  ggrepel                0.8.1      2019-05-07 [2]
##  ggridges               0.5.2      2020-01-12 [2]
##  ggthemes               4.2.0      2019-05-13 [1]
##  globals                0.12.5     2019-12-07 [2]
##  glue                   1.3.2      2020-03-12 [1]
##  gplots                 3.0.1.2    2020-01-11 [2]
##  gridExtra              2.3        2017-09-09 [2]
##  gtable                 0.3.0      2019-03-25 [2]
##  gtools                 3.8.1      2018-06-26 [2]
##  haven                  2.3.1      2020-06-01 [1]
##  hexbin                 1.28.0     2019-11-11 [2]
##  hms                    0.5.3      2020-01-08 [1]
##  htmltools              0.5.1.1    2021-01-22 [1]
##  htmlwidgets            1.5.1      2019-10-08 [2]
##  httr                   1.4.2      2020-07-20 [1]
##  ica                    1.0-2      2018-05-24 [2]
##  igraph                 1.2.5      2020-03-19 [1]
##  IRanges                2.20.2     2020-01-13 [2]
##  irlba                  2.3.3      2019-02-05 [2]
##  jsonlite               1.7.1      2020-09-07 [1]
##  KernSmooth             2.23-16    2019-10-15 [3]
##  knitr                  1.26       2019-11-12 [2]
##  knn.covertree          1.0        2019-10-28 [1]
##  labeling               0.3        2014-08-23 [2]
##  laeken                 0.5.1      2020-02-05 [1]
##  lattice                0.20-38    2018-11-04 [3]
##  lazyeval               0.2.2      2019-03-15 [2]
##  leiden                 0.3.1      2019-07-23 [2]
##  lifecycle              0.2.0      2020-03-06 [1]
##  listenv                0.8.0      2019-12-05 [2]
##  lmtest                 0.9-37     2019-04-30 [2]
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##  magrittr             * 2.0.1      2020-11-17 [1]
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##  metap                  1.2        2019-12-08 [2]
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##  pkgconfig              2.0.3      2019-09-22 [1]
##  pkgload                1.0.2      2018-10-29 [2]
##  plotly                 4.9.1      2019-11-07 [2]
##  plotrix                3.7-7      2019-12-05 [2]
##  plyr                   1.8.5      2019-12-10 [2]
##  png                    0.1-7      2013-12-03 [2]
##  prettyunits            1.1.1      2020-01-24 [1]
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##  sessioninfo            1.1.1      2018-11-05 [2]
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##  sp                     1.4-2      2020-05-20 [1]
##  stringi                1.5.3      2020-09-09 [1]
##  stringr              * 1.4.0      2019-02-10 [1]
##  SummarizedExperiment   1.16.1     2019-12-19 [2]
##  survival               3.1-8      2019-12-03 [3]
##  testthat               2.3.2      2020-03-02 [1]
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##  TH.data                1.0-10     2019-01-21 [2]
##  tibble               * 3.0.4      2020-10-12 [1]
##  tidyr                * 1.1.2      2020-08-27 [1]
##  tidyselect             1.1.0      2020-05-11 [1]
##  tidyverse            * 1.3.0      2019-11-21 [2]
##  tsne                   0.1-3      2016-07-15 [2]
##  TTR                    0.23-6     2019-12-15 [1]
##  usethis                1.5.1      2019-07-04 [2]
##  uwot                   0.1.5      2019-12-04 [2]
##  vcd                    1.4-7      2020-04-02 [1]
##  vctrs                  0.3.5      2020-11-17 [1]
##  VIM                    6.0.0      2020-05-08 [1]
##  viridis              * 0.5.1      2018-03-29 [2]
##  viridisLite          * 0.3.0      2018-02-01 [2]
##  withr                  2.3.0      2020-09-22 [1]
##  xfun                   0.12       2020-01-13 [2]
##  xml2                   1.3.2      2020-04-23 [1]
##  xts                    0.12-0     2020-01-19 [1]
##  XVector                0.26.0     2019-10-29 [2]
##  yaml                   2.2.1      2020-02-01 [1]
##  zip                    2.0.4      2019-09-01 [1]
##  zlibbioc               1.32.0     2019-10-29 [2]
##  zoo                    1.8-7      2020-01-10 [2]
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## 
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library